In [1]:
import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
plt.style.use("fivethirtyeight")
%matplotlib inline

# For reading stock data from yahoo
from pandas_datareader.data import DataReader
import yfinance as yf
from pandas_datareader import data as pdr

yf.pdr_override()

# For time stamps
from datetime import datetime


# The tech stocks we'll use for this analysis
tech_list = ['AAPL', 'GOOG', 'MSFT', 'AMZN']

# Set up End and Start times for data grab
tech_list = ['AAPL', 'GOOG', 'MSFT', 'AMZN']

end = datetime.now()
start = datetime(end.year - 1, end.month, end.day)

for stock in tech_list:
    globals()[stock] = yf.download(stock, start, end)
    

company_list = [AAPL, GOOG, MSFT, AMZN]
company_name = ["APPLE", "GOOGLE", "MICROSOFT", "AMAZON"]

for company, com_name in zip(company_list, company_name):
    company["company_name"] = com_name
    
df = pd.concat(company_list, axis=0)
df.tail(10)

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Out[1]:
Open High Low Close Adj Close Volume company_name
Date
2023-04-25 104.910004 105.449997 102.449997 102.570000 102.570000 65026800 AMAZON
2023-04-26 105.040001 106.620003 104.099998 104.980003 104.980003 73803800 AMAZON
2023-04-27 108.160004 110.860001 106.800003 109.820000 109.820000 149961200 AMAZON
2023-04-28 107.730003 109.480003 104.330002 105.449997 105.449997 130565000 AMAZON
2023-05-01 104.949997 105.230003 101.820000 102.050003 102.050003 74728100 AMAZON
2023-05-02 101.470001 103.900002 101.150002 103.629997 103.629997 73469400 AMAZON
2023-05-03 103.739998 105.959999 103.279999 103.650002 103.650002 65051900 AMAZON
2023-05-04 104.040001 105.389999 103.309998 104.000000 104.000000 45345500 AMAZON
2023-05-05 104.269997 105.760002 103.550003 105.660004 105.660004 56912900 AMAZON
2023-05-08 105.040001 106.095001 104.709801 105.830002 105.830002 46863632 AMAZON
In [2]:
AAPL.describe()
Out[2]:
Open High Low Close Adj Close Volume
count 251.000000 251.000000 251.000000 251.000000 251.000000 2.510000e+02
mean 149.580359 151.576135 147.848566 149.824741 149.481433 7.789400e+07
std 11.075802 10.896641 11.328421 11.173551 11.210275 2.438126e+07
min 126.010002 127.769997 124.169998 125.019997 124.829399 3.519590e+07
25% 142.110001 143.854996 139.949997 142.464996 141.973434 6.224455e+07
50% 148.869995 150.919998 147.240005 149.350006 148.910004 7.340920e+07
75% 156.809998 158.250000 154.345001 157.059998 156.516861 8.765635e+07
max 173.750000 176.149994 173.119995 174.550003 173.995270 1.826020e+08
In [3]:
AAPL.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 251 entries, 2022-05-09 to 2023-05-08
Data columns (total 7 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Open          251 non-null    float64
 1   High          251 non-null    float64
 2   Low           251 non-null    float64
 3   Close         251 non-null    float64
 4   Adj Close     251 non-null    float64
 5   Volume        251 non-null    int64  
 6   company_name  251 non-null    object 
dtypes: float64(5), int64(1), object(1)
memory usage: 15.7+ KB
In [4]:
# Let's see a historical view of the closing price
plt.figure(figsize=(15, 10))
plt.subplots_adjust(top=1.25, bottom=1.2)

for i, company in enumerate(company_list, 1):
    plt.subplot(2, 2, i)
    company['Adj Close'].plot()
    plt.ylabel('Adj Close')
    plt.xlabel(None)
    plt.title(f"Closing Price of {tech_list[i - 1]}")
    
plt.tight_layout()
In [5]:
# Now let's plot the total volume of stock being traded each day
plt.figure(figsize=(15, 10))
plt.subplots_adjust(top=1.25, bottom=1.2)

for i, company in enumerate(company_list, 1):
    plt.subplot(2, 2, i)
    company['Volume'].plot()
    plt.ylabel('Volume')
    plt.xlabel(None)
    plt.title(f"Sales Volume for {tech_list[i - 1]}")
    
plt.tight_layout()
In [6]:
import plotly.graph_objs as go
import plotly.subplots as sp

ma_day = [10, 20, 50]

for ma in ma_day:
    for company in company_list:
        column_name = f"MA for {ma} days"
        company[column_name] = company['Adj Close'].rolling(ma).mean()
        
fig = sp.make_subplots(rows=2, cols=2, 
                       subplot_titles=("AAPL", "GOOG", "MSFT", "AMZN"))

fig.add_trace(go.Scatter(x=AAPL.index, y=AAPL['Adj Close'], name='Adj Close'), row=1, col=1)
fig.add_trace(go.Scatter(x=AAPL.index, y=AAPL['MA for 10 days'], name='MA for 10 days'), row=1, col=1)
fig.add_trace(go.Scatter(x=AAPL.index, y=AAPL['MA for 20 days'], name='MA for 20 days'), row=1, col=1)
fig.add_trace(go.Scatter(x=AAPL.index, y=AAPL['MA for 50 days'], name='MA for 50 days'), row=1, col=1)

fig.add_trace(go.Scatter(x=GOOG.index, y=GOOG['Adj Close'], name='Adj Close'), row=1, col=2)
fig.add_trace(go.Scatter(x=GOOG.index, y=GOOG['MA for 10 days'], name='MA for 10 days'), row=1, col=2)
fig.add_trace(go.Scatter(x=GOOG.index, y=GOOG['MA for 20 days'], name='MA for 20 days'), row=1, col=2)
fig.add_trace(go.Scatter(x=GOOG.index, y=GOOG['MA for 50 days'], name='MA for 50 days'), row=1, col=2)

fig.add_trace(go.Scatter(x=MSFT.index, y=MSFT['Adj Close'], name='Adj Close'), row=2, col=1)
fig.add_trace(go.Scatter(x=MSFT.index, y=MSFT['MA for 10 days'], name='MA for 10 days'), row=2, col=1)
fig.add_trace(go.Scatter(x=MSFT.index, y=MSFT['MA for 20 days'], name='MA for 20 days'), row=2, col=1)
fig.add_trace(go.Scatter(x=MSFT.index, y=MSFT['MA for 50 days'], name='MA for 50 days'), row=2, col=1)

fig.add_trace(go.Scatter(x=AMZN.index, y=AMZN['Adj Close'], name='Adj Close'), row=2, col=2)
fig.add_trace(go.Scatter(x=AMZN.index, y=AMZN['MA for 10 days'], name='MA for 10 days'), row=2, col=2)
fig.add_trace(go.Scatter(x=AMZN.index, y=AMZN['MA for 20 days'], name='MA for 20 days'), row=2, col=2)
fig.add_trace(go.Scatter(x=AMZN.index, y=AMZN['MA for 50 days'], name='MA for 50 days'), row=2, col=2)

fig.update_layout(height=600, width=800, title="Moving Averages")
fig.show()
In [8]:
fig, axs = plt.subplots(2, 2, figsize=(15, 10), sharex=True)
axs = axs.flatten()

for ax, company, name in zip(axs, company_list, tech_list):
    company['Daily Return'] = company['Adj Close'].pct_change()
    ax.plot(company['Daily Return'], linestyle='--', marker='o')
    ax.set_title(f"Daily Return for {name}")
    ax.set_xlabel(None)
    ax.legend()
    
fig.text(0.5, 0.04, 'Date', ha='center')
fig.tight_layout()

No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
In [9]:
closing_df = pdr.get_data_yahoo(tech_list, start=start, end=end)['Adj Close']

# Make a new tech returns DataFrame
tech_rets = closing_df.pct_change()
tech_rets.head()

[*********************100%***********************]  4 of 4 completed
Out[9]:
AAPL AMZN GOOG MSFT
Date
2022-05-09 NaN NaN NaN NaN
2022-05-10 0.016112 0.000643 0.013269 0.018595
2022-05-11 -0.051841 -0.032032 -0.005441 -0.033210
2022-05-12 -0.026894 0.014790 -0.007020 -0.019958
2022-05-13 0.031916 0.057276 0.029644 0.022596
In [10]:
sns.pairplot(tech_rets, kind='reg')
Out[10]:
<seaborn.axisgrid.PairGrid at 0x7fc5141c8ac0>
In [11]:
plt.figure(figsize=(12, 10))

plt.subplot(2, 2, 1)
sns.heatmap(tech_rets.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation of stock return')

plt.subplot(2, 2, 2)
sns.heatmap(closing_df.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation of stock closing price')
Out[11]:
Text(0.5, 1.0, 'Correlation of stock closing price')
In [12]:
rets = tech_rets.dropna()

fig, ax = plt.subplots(figsize=(10, 8))

ax.scatter(rets.mean(), rets.std(), s=100, alpha=0.5)
ax.set_xlabel('Expected return')
ax.set_ylabel('Risk')

for i, stock in enumerate(rets.columns):
    ax.annotate(stock, (rets.mean()[i], rets.std()[i]), xytext=(10, -10), 
                textcoords='offset points', fontsize=12, ha='center', va='top', 
                bbox=dict(boxstyle='round,pad=0.5', fc='white', alpha=0.5),
                arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))

plt.show()
In [13]:
# Get the stock quote
df = pdr.get_data_yahoo('AAPL', start='2012-01-01', end=datetime.now())
# Show teh data
df

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Out[13]:
Open High Low Close Adj Close Volume
Date
2012-01-03 14.621429 14.732143 14.607143 14.686786 12.500193 302220800
2012-01-04 14.642857 14.810000 14.617143 14.765714 12.567370 260022000
2012-01-05 14.819643 14.948214 14.738214 14.929643 12.706893 271269600
2012-01-06 14.991786 15.098214 14.972143 15.085714 12.839727 318292800
2012-01-09 15.196429 15.276786 15.048214 15.061786 12.819364 394024400
... ... ... ... ... ... ...
2023-05-02 170.089996 170.350006 167.539993 168.539993 168.539993 48425700
2023-05-03 169.500000 170.919998 167.160004 167.449997 167.449997 65136000
2023-05-04 164.889999 167.039993 164.309998 165.789993 165.789993 81235400
2023-05-05 170.979996 174.300003 170.759995 173.570007 173.570007 113316400
2023-05-08 172.479996 173.850006 172.110001 173.500000 173.500000 55962793

2855 rows × 6 columns

In [16]:
import mplfinance as mpf

mpf.plot(df, type='candle', figsize=(16,6), title='Close Price History', ylabel='Close Price USD ($)')

/Users/koushal/opt/anaconda3/lib/python3.9/site-packages/mplfinance/_arg_validators.py:83: UserWarning:



 ================================================================= 

   WARNING: YOU ARE PLOTTING SO MUCH DATA THAT IT MAY NOT BE
            POSSIBLE TO SEE DETAILS (Candles, Ohlc-Bars, Etc.)
   For more information see:
   - https://github.com/matplotlib/mplfinance/wiki/Plotting-Too-Much-Data
   
   TO SILENCE THIS WARNING, set `type='line'` in `mpf.plot()`
   OR set kwarg `warn_too_much_data=N` where N is an integer 
   LARGER than the number of data points you want to plot.

 ================================================================
In [17]:
# Create a new dataframe with only the 'Close column 
data = df.filter(['Close'])
# Convert the dataframe to a numpy array
dataset = data.values
# Get the number of rows to train the model on
training_data_len = int(np.ceil( len(dataset) * .95 ))

training_data_len
Out[17]:
2713
In [18]:
# Scale the data
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(dataset)

scaled_data
Out[18]:
array([[0.00439887],
       [0.00486851],
       [0.00584391],
       ...,
       [0.90348828],
       [0.94978066],
       [0.94936411]])
In [19]:
train_data = scaled_data[0:int(training_data_len), :]
x_train = [train_data[i-60:i, 0] for i in range(60, len(train_data))]
y_train = [train_data[i, 0] for i in range(60, len(train_data))]
x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
In [20]:
from keras.models import Sequential
from keras.layers import Dense, LSTM

# Build the LSTM model
model = Sequential()
model.add(LSTM(128, return_sequences=True, input_shape= (x_train.shape[1], 1)))
model.add(LSTM(64, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
model.fit(x_train, y_train, batch_size=1, epochs=1)

2023-05-08 20:31:39.282130: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-05-08 20:31:44.792114: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-05-08 20:31:44.795121: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-05-08 20:31:44.796909: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-05-08 20:31:45.049594: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-05-08 20:31:45.051160: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-05-08 20:31:45.053191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-05-08 20:31:45.535504: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-05-08 20:31:45.537243: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-05-08 20:31:45.539540: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-05-08 20:31:45.780893: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-05-08 20:31:45.782640: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-05-08 20:31:45.784938: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-05-08 20:31:46.702427: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-05-08 20:31:46.704825: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-05-08 20:31:46.706623: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-05-08 20:31:46.946356: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-05-08 20:31:46.948606: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-05-08 20:31:46.950621: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]

2653/2653 [==============================] - 59s 21ms/step - loss: 0.0015
Out[20]:
<keras.callbacks.History at 0x7fc4fbee2d90>
In [21]:
# Create the testing data set
test_data = scaled_data[training_data_len - 60: , :]
x_test = np.array([test_data[i-60:i, 0] for i in range(60, len(test_data))])
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
y_test = dataset[training_data_len:, :]

# Get the models predicted price values 
predictions = scaler.inverse_transform(model.predict(x_test))

# Get the root mean squared error (RMSE)
rmse = np.sqrt(np.mean(((predictions - y_test) ** 2)))
rmse

2023-05-08 20:33:23.306279: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-05-08 20:33:23.308767: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-05-08 20:33:23.310555: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-05-08 20:33:23.589490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-05-08 20:33:23.591454: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-05-08 20:33:23.593154: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]

5/5 [==============================] - 1s 21ms/step
Out[21]:
6.169589188626046
In [22]:
import plotly.graph_objs as go

train = data[:training_data_len]
valid = data[training_data_len:]
valid['Predictions'] = predictions

fig = go.Figure()
fig.add_trace(go.Scatter(x=train.index, y=train['Close'], mode='lines', name='Train'))
fig.add_trace(go.Scatter(x=valid.index, y=valid['Close'], mode='lines', name='Val'))
fig.add_trace(go.Scatter(x=valid.index, y=valid['Predictions'], mode='lines', name='Predictions'))
fig.update_layout(title='Model', xaxis_title='Date', yaxis_title='Close Price USD ($)', legend=dict(x=0, y=1, traceorder='normal'))
fig.show()

/var/folders/_f/_g7s1mjs4hb2bh8q33tftrdw0000gn/T/ipykernel_76201/3303336320.py:5: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [23]:
valid
Out[23]:
Close Predictions
Date
2022-10-13 142.990005 147.478699
2022-10-14 138.380005 147.207199
2022-10-17 142.410004 146.703247
2022-10-18 143.750000 146.700012
2022-10-19 143.860001 147.089630
... ... ...
2023-05-02 168.539993 170.960602
2023-05-03 167.449997 171.526566
2023-05-04 165.789993 171.787872
2023-05-05 173.570007 171.647903
2023-05-08 173.500000 172.344437

142 rows × 2 columns